The authors highlight the refinement advantage over baseline and TrustScore [1]} by employing AUPR, AUROC, etc. We utilize the official source code and train VGG-16 [2]} with batch normalization. We retain \(10\%\)  of training data to validate CFN training parameters and report the calibration and refinement results on the official test split for CIFARs [3]}. The results are reported over 3 independent runs of the experiment.
